Student score prediction method based on big data analysis

Abstract

The invention discloses a student score prediction method based on big data analysis. The method is applied to the field of big data processing, and in order to solve the problems that in the prior art, judgment results are large in difference and do not have generalization and replicability, the method comprises the steps that firstly, data sets related to daily life of students are selected, andthen abnormal data removing processing is conducted on the selected data sets; s2, screening data, and obtaining data related to student examination scores based on a correlation analysis theory forthe data processed in the step S1; finally, regression analysis is carried out, a modeling method is determined according to the related data or the data type of student scores, and a student score prediction model is obtained; according to the method, the examination score prediction result of the corresponding student can be accurately obtained.

Description

technical field [0001] The invention belongs to the field of big data processing, and in particular relates to a teaching data analysis and processing technology. Background technique [0002] In the traditional education and teaching process, teachers generally use students' classroom performance, homework completion and quality, and stage test scores in the process to artificially and intuitively judge students' future test scores, lacking specific data and relevant statistical theoretical basis Support, resulting in subjective judgment results, not based on comprehensive consideration of data, resulting in a large deviation between the judgment results and the actual situation. At the same time, the subjective judgment results, due to the different experiences of individual teachers, the judgment results vary greatly, and are not scalable or replicable. Contents of the invention [0003] In order to solve the above technical problems, the present invention proposes a s...

AI Technical Summary

Problems solved by technology

[0002] In the traditional education and teaching process, teachers generally use students' classroom performance, homework completion and quality, and stage test scores in the process to artificially and intuitively judge students' future test scores, lacking specific data and relevant statistical theoretical basis Support, resulting in subjective judgment results and no comprehensive consideration based on data, resulting in a large deviation between the judgment results and the actual situation

At the same time, the subjective judgment results, due to the different experience of the individual teachers, the judgment results vary greatly, and they are not scalable or replicable.

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